An adaptation of a F-measure for automatic text summarization by extraction

2020 ◽  
Vol 23 (3) ◽  
pp. 2389-2398
Author(s):  
Mohamed Amine Boudia ◽  
Reda Mohamed Hamou ◽  
Abdelmalek Amine ◽  
Ahmed Chaouki Lokbani
Keyword(s):  
Text Summarization ◽  
Automatic Text Summarization ◽  
Automatic Text ◽  
F Measure

scholarly journals Prediction and Analysis of Extracting Relations using Spacy Model

2020 ◽  
Vol 8 (6) ◽  
pp. 3281-3287
Keyword(s):  
Natural Language ◽  
Information Extraction ◽  
Performance Measures ◽  
Text Summarization ◽  
Language Understanding ◽  
Language Generation ◽  
Automatic Text Summarization ◽  
Structured Information ◽  
Automatic Text ◽  
F Measure

Text is an extremely rich resources of information. Each and every second, minutes, peoples are sending or receiving hundreds of millions of data. There are various tasks involved in NLP are machine learning, information extraction, information retrieval, automatic text summarization, question-answered system, parsing, sentiment analysis, natural language understanding and natural language generation. The information extraction is an important task which is used to find the structured information from unstructured or semi-structured text. The paper presents a methodology for extracting the relations of biomedical entities using spacy. The framework consists of following phases such as data creation, load and converting the data into spacy object, preprocessing, define the pattern and extract the relations. The dataset is downloaded from NCBI database which contains only the sentences. The created model evaluated with performance measures like precision, recall and f-measure. The model achieved 87% of accuracy in retrieving of entities relation.

A New Metric of Validation for Automatic Text Summarization by Extraction

2017 ◽  
Vol 8 (3) ◽  
pp. 20-40 ◽  
Author(s):  
Ahmed Chaouki Lokbani
Keyword(s):  
Hybrid Method ◽  
Text Summarization ◽  
Automatic Summarization ◽  
Automatic Text Summarization ◽  
Binary Weighted ◽  
Automatic Text ◽  
F Measure

In this article, the author proposes a new metric of evaluation for automatic summaries of texts. In this case, the adaptation of the F-measure that generates a hybrid method of evaluating an automatic summary at the same time as both extrinsic and intrinsic. The article starts by studying the feasibility of adaptation of the F-measure for the evaluation of automatic summarization. After that, the author defines how to calculate the F-measure for a candidate summary. Text is presented with a term vector which can be either a word or a phrase, with a binary-weighted or occurrence. Finally, to determine to the exactitude of evaluation of the F-measure for automatic summarization by extraction calculates correlation with the ROUGE Evaluation.

scholarly journals A SYNTACTIC-BASED SENTENCE VALIDATION TECHNIQUE FOR MALAY TEXT SUMMARIZER

2021 ◽  
Vol 20 (Number 3) ◽  
pp. 329-352
Author(s):  
Suraya Alias ◽  
Mohd Shamrie Sainin ◽  
Siti Khaotijah Mohammad
Keyword(s):  
Language Processing ◽  
Text Summarization ◽  
Compression Rate ◽  
Automatic Evaluation ◽  
Readability Score ◽  
Automatic Text Summarization ◽  
Validation Technique ◽  
Automatic Text ◽  
F Measure

In the Automatic Text Summarization domain, a Sentence Compression (SC) technique is applied to the summary sentence to remove unnecessary words or phrases. The purpose of SC is to preserve the important information in the sentence and to remove the unnecessary ones without sacrificing the sentence's grammar. The existing development of Malay Natural Language Processing (NLP) tools is still under study with limited open access. The issue is the lack of a benchmark dataset in the Malay language to evaluate the quality of the summaries and to validate the compressed sentence produced by the summarizer model. Hence, our paper outlines a Syntactic-based Sentence Validation technique for Malay sentences by referring to the Malay Grammar Pattern. In this work, we propose a new derivation set of Syntactic Rules based on the Malay main Word Class to validate a Malay sentence that undergoes the SC procedure. We experimented using the Malay dataset of 100 new articles covering the Natural Disaster and Events domain to find the optimal compression rate and its effect on the summary content. An automatic evaluation using ROUGE (Recall-Oriented Understudy for Gisting Evaluation) produced a result with an average F-measure of 0.5826 and an average Recall value of 0.5925 with an optimum compression rate of 0.5 Confidence Conf value. Furthermore, a manual summary evaluation by a group of Malay experts on the grammaticality of the compressed summary sentence produced a good result of 4.11 and a readability score of 4.12 out of 5. This depicts the reliability of the proposed technique to validate the Malay sentence with promising summary content and readability results.

scholarly journals Single Document Automatic Text Summarization using Term Frequency-Inverse Document Frequency (TF-IDF)

2016 ◽  
Vol 7 (4) ◽  
pp. 285 ◽  
Author(s):  
Hans Christian ◽  
Mikhael Pramodana Agus ◽  
Derwin Suhartono
Keyword(s):  
Language Processing ◽  
Text Summarization ◽  
The Other ◽  
Online Information ◽  
Inverse Document Frequency ◽  
Automatic Text Summarization ◽  
Document Frequency ◽  
Online Source ◽  
Automatic Text ◽  
F Measure

The increasing availability of online information has triggered an intensive research in the area of automatic text summarization within the Natural Language Processing (NLP). Text summarization reduces the text by removing the less useful information which helps the reader to find the required information quickly. There are many kinds of algorithms that can be used to summarize the text. One of them is TF-IDF (TermFrequency-Inverse Document Frequency). This research aimed to produce an automatic text summarizer implemented with TF-IDF algorithm and to compare it with other various online source of automatic text summarizer. To evaluate the summary produced from each summarizer, The F-Measure as the standard comparison value had been used. The result of this research produces 67% of accuracy with three data samples which are higher compared to the other online summarizers.

scholarly journals Peringkasan Teks Otomatis pada Modul Pembelajaran Berbahasa Indonesia Menggunakan Metode Cross Latent Semantic Analysis (CLSA)

2021 ◽  
Vol 7 (2) ◽  
pp. 153
Author(s):  
Yunita Maulidia Sari ◽  
Nenden Siti Fatonah
Keyword(s):  
Latent Semantic Analysis ◽  
Semantic Analysis ◽  
Text Summarization ◽  
Compression Rate ◽  
Inverse Document Frequency ◽  
Term Frequency ◽  
Automatic Text Summarization ◽  
Document Frequency ◽  
Automatic Text ◽  
F Measure

Perkembangan teknologi yang pesat membuat kita lebih mudah dalam menemukan informasi-informasi yang dibutuhkan. Permasalahan muncul ketika informasi tersebut sangat banyak. Semakin banyak informasi dalam sebuah modul maka akan semakin panjang isi teks dalam modul tersebut. Hal tersebut akan memakan waktu yang cukup lama untuk memahami inti informasi dari modul tersebut. Salah satu solusi untuk mendapatkan inti informasi dari keseluruhan modul dengan cepat dan menghemat waktu adalah dengan membaca ringkasannya. Cara cepat untuk mendapatkan ringkasan sebuah dokumen adalah dengan cara peringkasan teks otomatis. Peringkasan teks otomatis (Automatic Text Summarization) merupakan teks yang dihasilkan dari satu atau lebih dokumen, yang mana hasil teks tersebut memberikan informasi penting dari sumber dokumen asli, serta secara otomatis hasil teks tersebut tidak lebih panjang dari setengah sumber dokumen aslinya. Penelitian ini bertujuan untuk menghasilkan peringkasan teks otomatis pada modul pembelajaran berbahasa Indonesia dan mengetahui hasil akurasi peringkasan teks otomatis yang menerapkan metode Cross Latent Semantic Analysis (CLSA). Jumlah data yang digunakan pada penelitian ini sebanyak 10 file modul pembelajaran yang berasal dari modul para dosen Universitas Mercu Buana, dengan format .docx sebanyak 5 file dan format .pdf sebanyak 5 file. Penelitian ini menerapkan metode Term Frequency-Inverse Document Frequency (TF-IDF) untuk pembobotan kata dan metode Cross Latent Semantic Analysis (CLSA) untuk peringkasan teks. Pengujian akurasi pada peringkasan modul pembelajaran dilakukan dengan cara membandingkan hasil ringkasan manual oleh manusia dan hasil ringkasan sistem. Yang mana pengujian ini menghasilkan rata-rata nilai f-measure, precision, dan recall tertinggi pada compression rate 20% dengan nilai berturut-turut 0.3853, 0.432, dan 0.3715.

A Quantum-Inspired Genetic Algorithm for Extractive Text Summarization

2021 ◽  
Vol 10 (2) ◽  
pp. 42-60
Author(s):  
Khadidja Chettah ◽  
Amer Draa
Keyword(s):  
Genetic Algorithm ◽  
State Of The Art ◽  
Text Summarization ◽  
Automated System ◽  
Evaluation Metrics ◽  
Document Summarization ◽  
Automatic Text Summarization ◽  
Reference Methods ◽  
Textual Data ◽  
Automatic Text

Automatic text summarization has recently become a key instrument for reducing the huge quantity of textual data. In this paper, the authors propose a quantum-inspired genetic algorithm (QGA) for extractive single-document summarization. The QGA is used inside a totally automated system as an optimizer to search for the best combination of sentences to be put in the final summary. The presented approach is compared with 11 reference methods including supervised and unsupervised summarization techniques. They have evaluated the performances of the proposed approach on the DUC 2001 and DUC 2002 datasets using the ROUGE-1 and ROUGE-2 evaluation metrics. The obtained results show that the proposal can compete with other state-of-the-art methods. It is ranked first out of 12, outperforming all other algorithms.

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